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Diagnosis of gastric inflammation and malignancy in endoscopic biopsies based on Fourier transform infrared spectroscopy.

Chronic gastric diseases are common in China, and this may lead to a higher incidence of stomach carcinoma than is found in Western countries. Gastric endoscopy and biopsy analysis are performed frequently. Patients may suffer from gastric carcinoma, chronic superficial gastritis, or chronic atrophic gastritis. Chronic superficial gastritis is a mild inflammation with glandular preservation. Chronic atrophic gastritis may have pathologic characteristics such as gland dropout and precancerous changes. This is important because this kind of gastritis could develop into carcinoma if the disease is not closely monitored. It therefore is important to classify biopsies after endoscopy because this categorization could help determine proper medical treatment.

Fourier transform infrared (FT-IR) (3) spectroscopy can effectively provide chemical information concerning the structure and composition of biological materials at the molecular level; the application of vibrational spectroscopy is therefore expanding (1-5). This noninvasive, convenient, and rapid technique has been applied to the study of various types of healthy and malignant tissues and is a powerful tool in researching the biochemistry of cancer (6-11). The technique also has promising potential for detecting early cancer. Our research group has successfully used FT-IR spectroscopy to diagnose multiple kinds of carcinoma, such as stomach, colon, liver, esophagus, lung, gallbladder, breast, and parotid gland (12-24).

We previously performed FT-IR analysis on a large number of samples obtained at surgery from paired carcinomas and adjacent healthy tissues (diameter, 1-2 cm). The results showed that there were significant differences between the spectra of malignant and corresponding healthy tissues and that a series of discrimination regularities existed (25). In addition, our previous results revealed that FT-IR spectroscopy could detect molecular abnormalities that occurred before the change in morphology seen under the light microscope (25). FT-IR technology thus makes it possible to detect inflammatory and precancerous stages. The FT-IR method has the possibility of developing into a new technique for gastric endoscopic examination. We believe that noninvasive, rapid, accurate, and convenient analysis of gastroscopic tissues can be performed with Fourier-transform mid-infrared spectroscopy if the mid-infrared fiber optics and stomach endoscopy technologies can be combined successfully, but a flexible mid-infrared optical fiber and miniprobe are not yet available. The aim of this study, therefore, was to explore the use of FT-IR spectroscopy, combined with multivariate analysis method, in the classification of gastric endoscopic biopsies.

Materials and Methods

PATIENTS

A total of 103 gastric tissue specimens were obtained by the Medical Division of the First Hospital of Xi'an Jiaotong University, China. Informed consent was obtained from each patient before the study. One endoscopic pinch biopsy (diameter, 1-3 mm) was obtained from each patient. According to the results from the pathology review, the studied samples consisted of 19 cases of cancer, 35 cases of chronic atrophic gastritis, 29 cases of chronic superficial gastritis, and 20 samples of healthy stomach tissue.

INSTRUMENT

Because the endoscopic samples are small and it is hard to obtain a high-quality IR spectrum, a modified attenuated total reflectance (ATR) accessory was linked to a WQD500 FT-IR spectrometer (Beijing No. 2 Optical Instrument Factory). The modified ATR accessory was constructed with a ZnSe crystal. The improvement in the ATR accessory involves reducing the number of internal reflectances to five, which can decrease the attenuation of light energy and provide higher light throughput, thus increasing the signal-to-noise ratio. The FT-IR spectrometer was equipped with a mercury cadmium telluride detector cooled by liquid nitrogen, which has higher stability and sensitivity than the traditional deuterated triglycinate sulfate detector.

SPECTRAL MEASUREMENTS

The fresh surgically resected specimens were immediately noninvasively analyzed with the mobile WQD-500 FT-IR spectrometer. Each sample was placed on the ATR accessory for analysis. To collect the data for each spectrum, we performed 32 coadded scans at a resolution of 4 cm-1, with a typical range from 800 to 4000 cm-1. It took -1-2 min to obtain the spectrum noninvasively. For comparison, after the spectra for the samples were recorded, the fixed samples were stored in liquid nitrogen and sent for histologic examination for comparison with the spectral analysis. Each IR result was compared with a biopsy from the same tissue sample.

MULTIVARIATE STATISTICAL ANALYSIS

After FT-IR spectra were collected, and in parallel, a pathology-based diagnosis was made using the routine method of hematoxylin/eosin staining for the property of interest. In the second step, we statistically analyzed the obtained FT-IR spectra to determine differences and convert the spectroscopic data into clinically useful information.

We used OMNIC5.0 as the data-processing software. The five-point moving average smoothing method was adopted for each spectrum to reduce the random noise in the data. To achieve more accurate differences in the ratio of peak intensities, we first performed baseline corrections of the spectra. For each spectral feature, such as the relative intensities of peaks A and B, we performed baseline corrections between the peaks being compared to sharpen the spectral differences among different kinds of tissues. For the purpose of multiple group classifications, we performed a supervised linear discriminant analysis (LDA), using the SPSS 10.0 statistical package (26) for the LDA algorithm.

For a supervised LDA, the class identity was known and used in the calculation. The LDA algorithm was derived from an equation (27):

Z = [w.sub.1][x.sub.1] + [w.sub.2] [X.sub.2] + [w.sub.3][x.sub.3] + ... + [w.sub.i][x.sub.i]

where Z is the discriminant score, and [w.sub.1] is the discriminant weight for the ith independent variable [x.sub.i]. In this study, the four classifications of gastric endoscopic biopsies were identified by LDA algorithms with seven independent variables, which were the intensity ratios of ~1640 [cm.sup.-1] (peak A) vs ~1550[cm.sup.-1] (peak B), ~1460 cm-1 (peak C) vs ~1400[cm.sup.-1] (peak D), ~1310[cm.sup.-1] (peak E) vs ~1240[cm.sup.-1] (peak F), and ~1160[cm.sup.-1] (peak G) vs ~1120[cm.sup.-1] (peak H), and the peak positions of the amide I band plus water (peak A), amide II band (peak B), and ~1310[cm.sup.-1] band (peak E), respectively. Because the number of samples for each group was not large enough, all spectra were split into a training set to train the LDA models, and a leave-one-out cross-validation was used to assess the quality of the discriminant model. The class assignment of any given spectrum was derived by computing its distance from all class centroids and allotting it to the class whose centroid was nearest.

Results

In the endoscopic biopsies, the majority of spectral peaks were in the 1000-1800[cm.sup.-1] region. The mean FT-IR absorption spectra for the gastric endoscopic biopsies in the regions 1800-1480[cm.sup.-1] and 1480-1000[cm.sup.-1] are shown in Fig. 1. Similar to previous results, there was distinctive absorption at peaks A through H, which were selected as the seven independent variables for multivariate discriminant analysis.

[FIGURE 1 OMITTED]

The statistical results for the intensity ratios [mean (SD)] for peaks A/B, C/D, E/F, and G/H and the mean (SD) peak positions for A, B, and E in the FT-IR spectra for different types of gastric endoscopic samples are listed in Table 1. We observed that the change in the regularities of the spectral characteristics agreed with the type of disease. Therefore, all of the characteristics listed above needed to be considered simultaneously for the classification of gastric tissues obtained from endoscopic examination.

In this study, we first used three discriminant functions in the analysis and used three-dimensional scores for each sample for classification. The classification results for all four groups of gastric specimens in the training set and the cross-validation study are shown in Table 2. The results based on the FT-IR spectra with the LDA analysis, with reference to the histologic diagnoses, are shown in Table 3. The LDA-supervised classification was very promising, with 84% overall accuracy for the training set. For the cross-validation, each case was classified by the first three discriminant functions derived from all cases other than that individual case, with an overall accuracy of 77% correctly classified for the cross-validated grouped cases. The sensitivities for FT-IR spectroscopic classification of gastric cancer, chronic atrophic gastritis, superficial gastritis, and healthy tissue were 74%, 66%, 90%, and 90%, respectively.

Discussion

The spectral characteristics of peaks A-H in the FT-IR spectra differed among healthy, chronic superficial gastritis, atrophic gastritis, and cancerous gastric specimens. Significant shifts of the peak frequencies and changes in the intensity ratios occurred, as shown in Table 1 and Fig. 1. The spectral characteristics of the small gastric biopsies were similar to those of block gastric tissues, which were obtained during surgery and measured in our previous studies. The spectral features of malignant and healthy gastric tissues were in good agreement with the criteria established in our previous work (21).

These spectral features are related to the changes in structure and composition of biological molecules such as proteins, nucleic acids, and fats. As shown in Fig. 1, the shape of spectra for chronic atrophic gastritis exhibited some of the same characteristic bands as cancer. Chronic superficial gastritis is a milder inflammation, and the shape of the spectrum for this condition was similar to that of healthy gastric tissue. The prominent band at ~1640[cm.sup.-1] belongs to the amide I band of protein and the H-O-H deformation vibration of water; the -1550 cm-1 absorption peak arises from N-H bending and C-N stretching (amide II band) in proteins. In the spectra of gastric cancer biopsies, the ratios of the intensities of peak A to peak B were higher in the malignant tissue because the intensity of peak A was stronger as a result of the higher water content in cancerous tissue. In addition, the strength of the amide II bands gradually increased from chronic atrophic gastritis to superficial gastritis to healthy tissue, as shown in Fig. 1.

The intensity of the -1400[cm.sup.-1] peak was stronger than that of the ~1460[cm.sup.-1] peak in the spectra of cancerous samples. For chronic atrophic gastritis, the decrease in intensity near 1460[cm.sup.-1] was not significant, i.e., the intensity of the band at ~1460[cm.sup.-1] became slightly less than, or approximately equal to, that of the peak at 1400[cm.sup.-1]. In the spectra of superficial gastritis samples, the intensity of the peak at 1460[cm.sup.-1] was slightly stronger than that of the peak at 1400[cm.sup.-1], and the intensity of the peak at 1460[cm.sup.-1] was stronger than that of the peak at 1400[cm.sup.-1] in healthy tissue.

In the spectra for cancer, the intensity of the absorption peak at ~1310[cm.sup.-1] increased, and the peak position shifted to a lower wavenumber. Compared with the spectra of the malignant tissues, the absorption peak near 1310[cm.sup.-1] was weaker in the spectra of atrophic gastritis samples. Similar to the gastric cancer tissues, the position of peak E often shifted to a lower wavenumber, which was different from chronic superficial gastritis. In healthy tissues, the peak at ~1240[cm.sup.-1] was stronger, and the band near 1318[cm.sup.-1] became weak and sometimes disappeared. In addition, the position of this band often shifted to a higher wavenumber.

The intensity of the band at ~1160[cm.sup.-1] was often less than that of the band at ~1120[cm.sup.-1] in the spectra of stomach cancer samples. In the healthy gastric tissues, the intensity of the peak near 1160[cm.sup.-1] increased and was often stronger than that of band at ~1120[cm.sup.-1].

Our results indicate that FT-IR spectroscopy, combined with appropriate pattern recognition algorithms, can distinguish benign from malignant disease for endoscopic gastric biopsies. There are several advantages of FT-IR spectroscopy: it is inexpensive, less time-consuming, does not require special sample preparation or any biochemical reagents, and uses only small amounts of sample, leaving sufficient material for other clinical tests. Moreover, it is a computer-operated system, which helps standardize interpretation of results. Real-time FT-IR spectroscopy of biopsy samples taken at the time of endoscopy can provide accurate, rapid diagnostic differentiation between healthy tissue, superficial and atrophic inflammation, and cancer, using a technique that is currently available. If a miniprobe can be developed for IR detection that can pass through the endoscopic biopsy channel and be coupled with fiber optics, clinicians would have a noninvasive real-time technique for directed instead of random biopsy analysis.

We thank the National Natural Science Foundation of China (Grant 30371604), the State Key Project for Fundamental Research of China (Grant 2002CCA01900), and the Doctoral Project of the High Education Ministry of China for supporting this work. We gratefully acknowledge Beijing No. 2 Optical Instrument Factory (Beijing, China) for providing the FT-IR spectrometer and for excellent technical assistance.

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QING-BO LI, [1] XUE-JUN SUN, [2] YI-ZHUANG XU, [1] LI-MIN YANG, [1] YUAN-FU ZHANG, [1] SHI-FU WENG, [1] DING-SEN SHI, [2] and JIN-GUANG WU [1] *

[1] The State Key Laboratory of Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing, Peoples Republic of China.

[2] Department of General Surgery, First Hospital of Xi'an Jiaotong University, Xi'an, Shanxi Province, Peoples Republic of China.

* Address correspondence to this author at: The State Key Laboratory of Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, Peoples Republic of China. Fax 86-10-62751708; e-mail wujg@pku.edu.cn.

Received June 1, 2004; accepted December 10, 2004. Previously published online at DOI: 10.1373/clinchem.2004.037986

[3] Nonstandard abbreviations: FT-IR, Fourier transform infrared; ATR, attenuated total reflectance; and LDA, linear discriminant analysis.
Table 1. Mean (SD) peak frequencies and intensity ratios for
healthy gastric tissue, chronic superficial gastritis, chronic
atrophic gastritis, and gastric cancer.

 Chronic
 superficial
 Healthy tissue gastritis
 (n = 20) (n = 29)

Peak frequency, [cm.sup.-1]
 A 1646 (2) 1642 (3)
 B 1553 (2) 1546 (2)
 E 1317 (6) 1316 (5)
Intensity ratio
 A/B 1.65 (0.19) 2.12 (0.26)
 C/D 1.10 (0.04) 1.40 (0.67)
 E/F 0.67 (0.09) 0.34 (0.21)
 G/H 1.22 (0.25) 0.91 (0.06)

 Chronic
 atrophic
 gastritis Cancer
 (n = 35) (n = 19)

Peak frequency, [cm.sup.-1]
 A 1640 (3) 1641 (2)
 B 1547 (2) 1549 (4)
 E 1306 (8) 1313 (3)
Intensity ratio
 A/B 2.33 (0.31) 2.48 (0.30)
 C/D 0.91 (0.34) 0.86 (0.10)
 E/F 0.52 (0.38) 0.79 (0.32)
 G/H 0.85 (0.12) 0.89 (0.13)

Table 2. Comparison of the FT-IR results with
histologic examination.

 Histologic examination

 Chronic
 atrophic
 FT-IR results Cancer gastritis

Training set
 Cancer 16 7
 Chronic atrophic gastritis 1 25
 Chronic superficial gastritis 2 3
 Healthy 0 0
Cross-validation
 Cancer 14 9
 Chronic atrophic gastritis 2 23
 Chronic superficial gastritis 3 3
 Healthy 0 0

 Histologic examination

 Chronic
 superficial
 FT-IR results gastritis Healthy

Training set
 Cancer 1 2
 Chronic atrophic gastritis 0 0
 Chronic superficial gastritis 28 0
 Healthy 0 18
Cross-validation
 Cancer 1 2
 Chronic atrophic gastritis 1 0
 Chronic superficial gastritis 26 0
 Healthy 1 18

Table 3. Results of statistical analysis of detection of endoscopic
gastric samples by FT-IR spectroscopy.

 Sensitivity, % Specificity, %

Training set
 Cancer 84 88
 Chronic atrophic gastritis 71 98
 Chronic superficial gastritis 97 93
 Healthy 90 100
Cross-validation
 Cancer 74 86
 Chronic atrophic gastritis 66 96
 Chronic superficial gastritis 90 92
 Healthy 90 99

 Predictive Predictive
 value of a value of a
 positive test, % negative test, %

Training set
 Cancer 62 96
 Chronic atrophic gastritis 96 87
 Chronic superficial gastritis 85 99
 Healthy 100 98
Cross-validation
 Cancer 54 94
 Chronic atrophic gastritis 88 84
 Chronic superficial gastritis 81 96
 Healthy 95 98
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Title Annotation:Cancer Diagnostics
Author:Li, Qing-Bo; Sun, Xue-Jun; Xu, Yi-Zhuang; Yang, Li-Min; Zhang, Yuan-Fu; Weng, Shi-Fu; Shi, Ding-Sen;
Publication:Clinical Chemistry
Date:Feb 1, 2005
Words:3490
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